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fix clipboard on linux
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parent
abcb4accbd
commit
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1
.gitignore
vendored
1
.gitignore
vendored
@ -3,3 +3,4 @@
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.RData
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.Ruserdata
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AMR.Rproj
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tests/testthat/Rplots.pdf
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10
.travis.yml
10
.travis.yml
@ -3,15 +3,9 @@
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# Setting up R deps
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language: r
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r: 3.2
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r_packages:
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- covr
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- testthat
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- dplyr
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- rvest
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- xml2
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- reshape2
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r_packages: covr
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# system deps
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# system deps, install xclip for clipboard support
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os:
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- linux
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- osx
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@ -1,6 +1,6 @@
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Package: AMR
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Version: 0.1.2
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Date: 2018-03-27
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Date: 2018-04-02
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Title: Antimicrobial Resistance Analysis
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Authors@R: c(
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person(
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@ -30,7 +30,7 @@ clipboard_import <- function(sep = '\t',
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on.exit(close(file))
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}
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import_tbl <- read.delim(file = file,
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import_tbl <- tryCatch(read.delim(file = file,
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sep = sep,
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header = header,
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strip.white = TRUE,
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@ -38,7 +38,19 @@ clipboard_import <- function(sep = '\t',
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na.strings = na,
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fileEncoding = 'UTF-8',
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encoding = 'UTF-8',
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stringsAsFactors = FALSE)
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stringsAsFactors = FALSE),
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error = function(e) {
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FALSE
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})
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if (import_tbl == FALSE) {
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cat("No clipboard content found.")
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if (Sys.info()['sysname'] %like% "Linux") {
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cat(" These functions do not work without X11 installed.")
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}
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cat("\n")
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return(invisible())
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}
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# use tibble, so column types will be translated correctly
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import_tbl <- as_tibble(import_tbl)
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@ -86,18 +98,22 @@ clipboard_export <- function(x,
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on.exit(close(file))
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}
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write.table(x = x,
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tryCatch(write.table(x = x,
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file = file,
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sep = sep,
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na = na,
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row.names = FALSE,
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col.names = header,
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dec = dec,
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quote = FALSE)
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quote = FALSE),
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error = function(e) {
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FALSE
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})
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if (info == TRUE) {
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cat("Successfully exported to clipboard:", NROW(x), "obs. of", NCOL(x), "variables.\n")
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}
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}
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is_Windows <- function() {
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@ -105,6 +121,10 @@ is_Windows <- function() {
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}
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check_xclip <- function() {
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if (!isTRUE(file.exists(Sys.which("xclip")[1L]))) {
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if (Sys.info()['sysname'] %like% "Linux") {
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stop("Please install Linux package xclip first.")
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} else {
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stop("Please install package xclip first (use `brew install xclip on macOS`).")
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}
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}
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}
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2
R/data.R
2
R/data.R
@ -125,7 +125,7 @@
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#'
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#' # Add first isolates to our dataset:
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#' my_data <- my_data %>%
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#' mutate(first_isolates = first_isolate(my_data, date, patient_id, bactid))
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#' mutate(first_isolates = first_isolate(my_data, "date", "patient_id", "bactid"))
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#'
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#' # -------- #
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#' # ANALYSIS #
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@ -20,12 +20,12 @@
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#'
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#' Determine first (weighted) isolates of all microorganisms of every patient per episode and (if needed) per specimen type.
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#' @param tbl a \code{data.frame} containing isolates.
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#' @param col_date column name of the result date (or date that is was received on the lab), supports tidyverse-like quotation
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#' @param col_patient_id column name of the unique IDs of the patients, supports tidyverse-like quotation
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#' @param col_bactid column name of the unique IDs of the microorganisms (should occur in the \code{\link{microorganisms}} dataset), supports tidyverse-like quotation
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#' @param col_date column name of the result date (or date that is was received on the lab)
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#' @param col_patient_id column name of the unique IDs of the patients
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#' @param col_bactid column name of the unique IDs of the microorganisms (should occur in the \code{\link{microorganisms}} dataset)
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#' @param col_testcode column name of the test codes. Use \code{col_testcode = NA} to \strong{not} exclude certain test codes (like test codes for screening). In that case \code{testcodes_exclude} will be ignored. Supports tidyverse-like quotation.
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#' @param col_specimen column name of the specimen type or group, supports tidyverse-like quotation
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#' @param col_icu column name of the logicals (\code{TRUE}/\code{FALSE}) whether a ward or department is an Intensive Care Unit (ICU), supports tidyverse-like quotation
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#' @param col_specimen column name of the specimen type or group
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#' @param col_icu column name of the logicals (\code{TRUE}/\code{FALSE}) whether a ward or department is an Intensive Care Unit (ICU)
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#' @param col_keyantibiotics column name of the key antibiotics to determine first \emph{weighted} isolates, see \code{\link{key_antibiotics}}. Supports tidyverse-like quotation.
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#' @param episode_days episode in days after which a genus/species combination will be determined as 'first isolate' again
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#' @param testcodes_exclude character vector with test codes that should be excluded (case-insensitive)
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@ -36,8 +36,8 @@
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#' @param ignore_I logical to determine whether antibiotic interpretations with \code{"I"} will be ignored when \code{type = "keyantibiotics"}, see Details
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#' @param points_threshold points until the comparison of key antibiotics will lead to inclusion of an isolate when \code{type = "points"}, see Details
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#' @param info print progress
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#' @param col_genus (deprecated, use \code{col_bactid} instead) column name of the genus of the microorganisms, supports tidyverse-like quotation
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#' @param col_species (deprecated, use \code{col_bactid} instead) column name of the species of the microorganisms, supports tidyverse-like quotation
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#' @param col_genus (deprecated, use \code{col_bactid} instead) column name of the genus of the microorganisms
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#' @param col_species (deprecated, use \code{col_bactid} instead) column name of the species of the microorganisms
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#' @details \strong{WHY THIS IS SO IMPORTANT} \cr
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#' To conduct an analysis of antimicrobial resistance, you should only include the first isolate of every patient per episode \href{https://www.ncbi.nlm.nih.gov/pubmed/17304462}{[1]}. If you would not do this, you could easily get an overestimate or underestimate of the resistance of an antibiotic. Imagine that a patient was admitted with an MRSA and that it was found in 5 different blood cultures the following week. The resistance percentage of oxacillin of all \emph{S. aureus} isolates would be overestimated, because you included this MRSA more than once. It would be \href{https://en.wikipedia.org/wiki/Selection_bias}{selection bias}.
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#'
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@ -57,11 +57,9 @@
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#'
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#' library(dplyr)
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#' my_patients$first_isolate <- my_patients %>%
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#' left_join_microorganisms() %>%
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#' first_isolate(col_date = date,
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#' col_patient_id = patient_id,
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#' col_genus = genus,
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#' col_species = species)
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#' first_isolate(col_date = "date",
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#' col_patient_id = "patient_id",
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#' col_bactid = "bactid")
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#'
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#' \dontrun{
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#'
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@ -122,16 +120,10 @@ first_isolate <- function(tbl,
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col_genus = NA,
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col_species = NA) {
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# support tidyverse-like quotation
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# col_date <- quasiquotate(deparse(substitute(col_date)), col_date)
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# col_patient_id <- quasiquotate(deparse(substitute(col_patient_id)), col_patient_id)
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# col_bactid <- quasiquotate(deparse(substitute(col_bactid)), col_bactid)
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# col_genus <- quasiquotate(deparse(substitute(col_genus)), col_genus)
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# col_species <- quasiquotate(deparse(substitute(col_species)), col_species)
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# col_testcode <- quasiquotate(deparse(substitute(col_testcode)), col_testcode)
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# col_specimen <- quasiquotate(deparse(substitute(col_specimen)), col_specimen)
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# col_icu <- quasiquotate(deparse(substitute(col_icu)), col_icu)
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# col_keyantibiotics <- quasiquotate(deparse(substitute(col_keyantibiotics)), col_keyantibiotics)
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# bactid OR genus+species must be available
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if (is.na(col_bactid) & (is.na(col_genus) | is.na(col_species))) {
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stop('`col_bactid or both `col_genus` and `col_species` must be available.')
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}
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# check if columns exist
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check_columns_existance <- function(column, tblname = tbl) {
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@ -141,7 +133,7 @@ first_isolate <- function(tbl,
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if (!is.na(column)) {
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if (!(column %in% colnames(tblname))) {
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stop('Column ', column, ' not found.')
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stop('Column `', column, '` not found.')
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}
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}
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}
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@ -156,7 +148,7 @@ first_isolate <- function(tbl,
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check_columns_existance(col_keyantibiotics)
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if (!is.na(col_bactid)) {
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tbl <- tbl %>% left_join_microorganisms()
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tbl <- tbl %>% left_join_microorganisms(by = col_bactid)
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col_genus <- "genus"
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col_species <- "species"
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}
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@ -41,7 +41,7 @@ clipboard_export(x, sep = "\\t", dec = ".", na = "", header = TRUE,
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data.frame
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}
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\description{
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These are helper functions around \code{\link{read.table}} and \code{\link{write.table}} to import from and export to clipboard. The data will be read and written as tab-separated by default, which makes it possible to copy and paste from other software like Excel and SPSS without further transformation.
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These are helper functions around \code{\link{read.table}} and \code{\link{write.table}} to import from and export to clipboard, with support for Windows, Linux and macOS. The data will be read and written as tab-separated by default, which makes it possible to copy and paste from other software like Excel and SPSS without further transformation.
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}
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\details{
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For \code{clipboard_export}, the reserved clipboard size for exporting will be set automatically to 125\% of the object size of \code{x}. This way, it is possible to export data with thousands of rows as the only limit will be your systems RAM.
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\arguments{
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\item{tbl}{a \code{data.frame} containing isolates.}
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\item{col_date}{column name of the result date (or date that is was received on the lab), supports tidyverse-like quotation}
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\item{col_date}{column name of the result date (or date that is was received on the lab)}
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\item{col_patient_id}{column name of the unique IDs of the patients, supports tidyverse-like quotation}
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\item{col_patient_id}{column name of the unique IDs of the patients}
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\item{col_bactid}{column name of the unique IDs of the microorganisms (should occur in the \code{\link{microorganisms}} dataset), supports tidyverse-like quotation}
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\item{col_bactid}{column name of the unique IDs of the microorganisms (should occur in the \code{\link{microorganisms}} dataset)}
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\item{col_testcode}{column name of the test codes. Use \code{col_testcode = NA} to \strong{not} exclude certain test codes (like test codes for screening). In that case \code{testcodes_exclude} will be ignored. Supports tidyverse-like quotation.}
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\item{col_specimen}{column name of the specimen type or group, supports tidyverse-like quotation}
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\item{col_specimen}{column name of the specimen type or group}
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\item{col_icu}{column name of the logicals (\code{TRUE}/\code{FALSE}) whether a ward or department is an Intensive Care Unit (ICU), supports tidyverse-like quotation}
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\item{col_icu}{column name of the logicals (\code{TRUE}/\code{FALSE}) whether a ward or department is an Intensive Care Unit (ICU)}
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\item{col_keyantibiotics}{column name of the key antibiotics to determine first \emph{weighted} isolates, see \code{\link{key_antibiotics}}. Supports tidyverse-like quotation.}
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@ -46,9 +46,9 @@ first_isolate(tbl, col_date, col_patient_id, col_bactid = NA,
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\item{info}{print progress}
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\item{col_genus}{(deprecated, use \code{col_bactid} instead) column name of the genus of the microorganisms, supports tidyverse-like quotation}
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\item{col_genus}{(deprecated, use \code{col_bactid} instead) column name of the genus of the microorganisms}
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\item{col_species}{(deprecated, use \code{col_bactid} instead) column name of the species of the microorganisms, supports tidyverse-like quotation}
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\item{col_species}{(deprecated, use \code{col_bactid} instead) column name of the species of the microorganisms}
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}
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\value{
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A vector to add to table, see Examples.
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@ -73,11 +73,9 @@ my_patients <- septic_patients
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library(dplyr)
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my_patients$first_isolate <- my_patients \%>\%
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left_join_microorganisms() \%>\%
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first_isolate(col_date = date,
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col_patient_id = patient_id,
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col_genus = genus,
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col_species = species)
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first_isolate(col_date = "date",
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col_patient_id = "patient_id",
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col_bactid = "bactid")
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\dontrun{
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@ -39,7 +39,7 @@ library(dplyr)
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# Add first isolates to our dataset:
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my_data <- my_data \%>\%
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mutate(first_isolates = first_isolate(my_data, date, patient_id, bactid))
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mutate(first_isolates = first_isolate(my_data, "date", "patient_id", "bactid"))
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# -------- #
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# ANALYSIS #
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Binary file not shown.
@ -1,9 +1,9 @@
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context("clipboard.R")
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test_that("clipboard works", {
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skip_on_os(c("linux", "solaris"))
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t1 <<- AMR::antibiotics # why is the <<- needed? Won't work without it...
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clipboard_export(t1, info = FALSE)
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t2 <- clipboard_import()
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skip_if(is.null(t1) | is.null(t2), message = "No clipboard content found: skipping.")
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expect_equal(t1, t2)
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})
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